Jessica Ann Roberts

Research

How do people make sense of complex visual information in informal and social contexts? How can interaction design facilitate productive learning talk and collaborative exploration? My work explores these questions through learning sciences research at the intersection of data visualization, human-computer interaction, and informal learning.

Current Work

Learning to See, Seeing to Learn: A Sociotechnical system supporting taxonomic identification activities in volunteer-based water quality biomonitoring.

Macroinvertebrates.org is a digital teaching collection of commonly found freshwater macroinvertebrates in the eastern United States. Through high-resolution gigapixel imagery and annotations of key diagnostic characters we aim to provide a resource to help water quality biomonitoring organizations train their citizen science volunteers to identify organisms used in water quality assessment. By improving supports for trainers and learners, we hope to improve citizen scientists' accuracy, confidence, and engagement in macroinvertebrate identification.

Representative Publications:

Roberts, J., Crowley, K., & Louw, M. (2018) Creating a Visual Representation of Expert Strategies to Inform the Design of Digital Tools for Citizen Science. In Proceedings of the 13th International Conference of the Learning Sciences. London, UK. [poster]

Past Projects

In this interdisciplinary collaboration between Northwestern University and the Field Museum of Natural History we investigated how multiple interface design iterations affect visitor engagement with exhibit artifacts.

CoCensus: Collaborative Exploration of U.S. Census Data

The CoCensus exhibit was an interactive census data map at the New York Hall of Science and Jane Addams Hull House Museum designed to support visitors in spatial, temporal, and quantitative reasoning about mapped census data. By allowing users to select data representing them from four categories of census data (heritage, household size, housing type, industry) and manipulate the aggregation level (census tract, borough, or city) and the decade of data (1990, 2000, and 2010) through embodied interaction, we studied how visitors can engage in open-ended explorations and conversations about data patterns. Research explored how interaction design facilitated visitors' learning talk as they made sense of the data together.

My dissertation research examined how competing interaction designs influence visitors' reasoning talk during group interactions. Through a 2x2 study design I examined the impacts off the means of control (full-body interactivity versus a handheld tablet controller) and the distribution of control (single input in which a control action affects all data simultaneously, and multi-input in which each user can manipulate his or her own data individually) on visitors' data talk and interactions in an in situ study. Findings from this study were reported in the International Journal of Computer Supported Collaborative Learning (Roberts & Lyons, 2017). I also investigated the spontaneous use of actor perspective taking (APT) during interactions and explored how APT was used by visitors to relate to the data. My methodology for measuring learning talk was awarded Best Paper at the 2017 International Conference of Computer Supported Collaborative Learning (CSCL).